Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently.Note to Practitioners—This work is motivated by the need for a review introducing soft robot modeling and control methods in parallel. Modeling and control play significant roles in robot research, and they are challenging especially for soft robots. The nonlinear and complex deformation of such robots necessitates specific modeling and control approaches. We introduce the state-of-the-art data-driven methods and survey three approaches widely utilized. This review also compares the performance of these methods, considering some important features like data amount requirement, control frequency, and target task. The features of each approach are summarized, and we discuss the possible future of this area.

In this paper, we propose a novel dynamic gait controller for the repetitive behavior of soft robot manipulators performing routine tasks. Compliance with soft robots is advantageous when the robot interacts with living organisms and other fragile objects. However, predicting and controlling repetitive behavior is challenging because of hysteresis and non-linear dynamics governing the interactions. Existing priorfree methods track the dynamic state using recurrent neural networks or rely on known generalized coordinates describing the robot's state. We propose to model the interaction induced by the repetitive behavior as gait dynamics and represent the dynamic state with Central Pattern Generator (CPG) tracking the motion phase and thus reduce the complexity of the robot's forward model. The proposed method bootstraps an ensemble of the forward models exploring multiple dynamic contexts that are expanded as it searches for repetitive motion producing the target repetitive behavior. The proposed approach is experimentally validated on a pneumatically actuated soft robot arm I-Support, where the method infers gaits for different targets.

Variable stiffness technologies have been widely adopted in soft robotics, also combining them with different actuation technologies and demonstrating the potential to increase the range of possible applications of soft systems, such as soft continuum manipulators. However, in most cases, the variable stiffness capabilities of these soft system are far from satisfying the application requirements. With the aim to explore new possibilities to fill this gap, in this work, we present a novel variable stiffness linear actuator (VSLA) based on the combination of composite fiber jamming and the inverse pneumatic artificial muscles working principle. The differential pressure driving approach adopted to control the VSLA decouples the actuator deformation from the variable stiffness capabilities, and this makes the VSLA a suitable candidate for the realization of new kind of continuous arms. Moreover, the VSLA novel architecture introduced in this article was analytically modeled considering the interaction among constituent elements and performances in different loading conditions have been computed. The results obtained from the experimental tests validated the model goodness in terms of assumptions made and showed remarkable variable stiffness capabilities, with a maximum jamming ratio that reaches 21.3.

Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate.

Designing and manufacturing actuators using soft materials are among the most important subjects for future robotics. In nature, animals made by soft tissues such as the octopus have attracted the attention of the robotics community in the last years. Suckers (or suction cups) are one of the most important and peculiar organs of the octopus body, giving it the ability to apply high forces on the external environment. The integration of suction cups in soft robots can enhance their ability to manipulate objects and interact with the environment similarly to what the octopus does. However, artificial suction cups are currently actuated using fluid pressure so most of them require external compressors, which will greatly increase the size of the soft robot. In this work, we proposed the use of the ElectroHydroDynamics (EHD) principle to actuate a suction cup. EHD is a fluidic phenomenon coupled with electrochemical reaction that can induce pressure through the application of a high-intensity electric field. We succeeded in developing a suction cup driven by EHD keeping the whole structure extremely simple, fabricated by using a 3D printer and a cutting plotter. We can control the adhesion of the suction cup by controlling the direction of the fluidic flow in our EHD pump. Thanks to a symmetrical arrangement of the electrodes, composed by plates parallel to the direction of the channel, we can change the direction of the flow by changing the sign of the applied voltage. We obtained the pressure of 643 Pa in one unit of EHD pump and pressure of 1428 Pa in five units of EHD pump applying 6 kV. The suction cup actuator was able to hold and release a 2.86 g piece of paper. We propose the soft actuator driven by the EHD pump, and expand the possibility to miniaturize the size of soft robots.

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This paper presents the concept design of a modular soft manipulator for minimally invasive surgery. Unlike traditional surgical manipulators based on metallic steerable needles, tendon-driven mechanisms, or articulated motorized links, we combine flexible fluidic actuators to obtain multidirectional bending and elongation with a variable stiffness mechanism based on granular jamming. The idea is to develop a manipulator based on a series of modules, each consisting of a silicone matrix with pneumatic chambers for 3-D motion, and one central channel for the integration of granular-jamming-based stiffening mechanism. A bellows-shaped braided structure is used to contain the lateral expansion of the flexible fluidic actuator and to increase its motion range. In this paper, the design and experimental characterization of a single module composed of such a manipulator is presented. Possible applications of the manipulator in the surgical field are discussed.

MRI compatibility, which often is a requirement for the new medical soft robot projects, greatly reduces available actuation methods and sensors. An example of such project is STIFF-FLOP, which aims to develop a soft silicone manipulator actuated by pressure. The current arm construction and method of actuation cause several undesirable effects, which pose problems for actuation and sensing. In this paper, the authors identify the source of those negative effects and propose improvements over the current construction to eliminate or limit their influence. The new construction concept is tested and compared with the current one. Possible ideas for further development are also proposed.

Soft Robotics is basically intended as building robots with highly compliant materials, but it is indeed more. Soft robots can safely interact with humans and with the environment and be able to adapt to different situations. These characteristics, combined with cheap materials and simple fabrication, candidate them to lead the next robotics revolution, when robots will massively move from the highly controlled industrial environments to the unpredictable real ones. As for the hardware, compliant materials such as silicones substitute stiff metals and rigid joints, following in under-actuated robots with virtually infinite number of degrees of freedom (DOF). Controlling this extremely high dexterity using the same approach of hard robotics (i.e. a hierarchical top-down control) simply does not apply. On the contrary, a new vision of the coupling between body and intelligence has to be adopted. Instead of considering brain and morphology separated entities (with the first commanding and the second obeying) morphological computation (Pfeifer and Bongard, 2007) proposes a radically different standpoint. The nonlinearity and complexity of the body dynamics rather than problems are considered as part of the solution, as the produced richness of behaviors allow the morphology to carry part of the computational load, thus simplifying the required (central) control instead of complicating it. This phenomenon is well observed in self-organization and embodiment of biological organisms, where characteristics like adaptability, robustness and agility are widely present without being explicitly controlled, and often without any (centralized) control. In the recent years many examples of embodiment in complex biological systems have been source of inspiration for robotics, ranging from cellular reproduction to the locomotion of more evolved animals like the salamander. A remarkable case where a central brain is present but still most of the sensory-motor control emerges from the body dynamics and the interaction with the environment is the octopus. The octopus (octopus vulgaris) is an invertebrate sea animal showing high dexterity, variable stiffness and much more complex behaviors than expected from its position in the evolutionary scale. Its body has virtually infinite DOF, resulting in a very high computational cost if pretended to be fully controlled by its central nervous system. It has been proved instead that its relatively simple brain mainly sends actuation patterns and most of the computation is performed by a combination of the peripheral nervous system, the dynamics of the compliant body and the interaction with the environment (Yekutieli et al., 2005). The result is an underactuated embodied system. The element at the base of this system is the muscular hydrostat. It is an isovolumetric structure widely present in nature as a component of compliant animal bodies. Besides generating the forces for movements it also provides skeletal support, featuring extremely high dexterity and variable stiffness ability. It is composed by a combination of muscular fibers arranged in longitudinal, transverse and oblique directions (Fig. 1). The different activation patterns of these

The new and promising field of soft robotics has many open areas of research such as the development of an exhaustive theoretical and methodological approach to dynamic modeling. To help contribute to this area of research, this paper develops a dynamic model of a continuum soft robot arm driven by cables and based upon a rigorous geometrically exact approach. The model fully investigates both dynamic interaction with a dense medium and the coupled tendon condition. The model was experimentally validated with satisfactory results, using a soft robot arm working prototype inspired by the octopus arm and capable of multibending. Experimental validation was performed for the octopus most characteristic movements: bending, reaching, and fetching. The present model can be used in the design phase as a dynamic simulation platform and to design the control strategy of a continuum robot arm moving in a dense medium.

This paper presents the concept design, the fabrication and the experimental characterization of a unit of a modular manipulator for minimal access surgery. Traditional surgical manipulators are usually based on metallic steerable needles, tendon driven mechanisms or articulated motorized links. In this work the main idea is to combine flexible fluidic actuators enabling omnidirectional bending and elongation capability and the granular jamming phenomenon to implement a selective stiffness changing. The proposed manipulator is based on a series of identical modules, each one consisting of a silicone tube with pneumatic chambers for allowing 3D motion and one central channel for the implementation of the granular jamming phenomenon for stiffening. The silicone is covered by a novel bellows-shaped braided structure maximizing the bending still limiting lateral expansion. In this paper one single module is tested in terms of bending range, elongation capability, generated forces and stiffness changing.

The octopus is a marine animal whose body has no rigid structures. It has eight arms mainly composed of muscles organized in a peculiar structure, named muscular hydrostat, that can change stiffness and that is used as a sort of a modifiable skeleton. Furthermore, the morphology of the arms and the mechanical characteristics of their tissues are such that the interaction with the environment, namely water, is exploited to simplify the control of movements. From these considerations, the octopus emerges as a paradigmatic example of embodied intelligence and a good model for soft robotics. In this paper the design and the development of an artificial muscular hydrostat are reported, underling the efforts in the design and development of new technologies for soft robotics, like materials, mechanisms, soft actuators. The first prototype of soft robot arm is presented, with experimental results that show its capability to perform the basic movements of the octopus arm (like elongation, shortening, and bending) and demonstrate how embodiment can be effective in the design of robots.

This paper examines the design and control of a robotic arm inspired by the anatomy and neurophysiology of Octopus vulgaris in light of embodiment theory. Embodiment in an animal is defined as the dynamic coupling between sensorymotor control, anatomy, materials, and the environment that allows for the animal to achieve effective behaviour. Octopuses in particular are highly embodied and dexterous animals: their arms are fully flexible, can bend in any direction, grasp objects and modulate stiffness along their length. In this paper the biomechanics and neurophysiology of octopus have been analysed to extract relevant information for use in the design and control of an embodied soft robotic arm. The embodied design requirements are firstly defined, and how the biology of the octopus meets these requirements presented. Next, a prototype continuum arm and control architecture based on octopus biology, and meeting the design criteria, are presented. Finally, experimental results are presented to show how the developed prototype arm is able to reproduce motions performed by live octopus for contraction, elongation, bending, and grasping.

Soft robots have significant advantages over traditional robots made of rigid materials. However, controlling this type of robot by conventional approaches is difficult. Reservoir computing has been demonstrated to be an effective approach for achieving rapid learning in benchmark tasks and conventional robots. In this study, we investigated the feasibility and capacity of the reservoir computing approach to embedding and switching between multiple behaviors in a on-line manner in a soft robotic arm. The result shows that this approach can successfully achieve this task.

Continuum structures with a very high or infinite number of degrees of freedom (DOF) are very interesting structures in nature. Mimicking this kind of structures artificially is challenging due to the high number of required DOF. This paper presents a kinematic and dynamic model for an underwater robotic manipulator inspired by Octopus vulgaris. Then, a prototype arm inspired by live octopus is presented and the model validated experimentally. Initial comparisons of simulated and experimental results show good agreement.